Profile Ultrasonic Velocity Measurements Performed on Slabbed Core: Implications for High-Resolution Permeability Prediction in Low-Permeability Rocks
Notice bibliographique
Résumé
Abstract Calibration of reservoir models for unconventional hydrocarbon reservoirs requires permeability data as input. Accurate permeability prediction from velocity data is desirable due to the relative abundance of velocity data that is typically available during exploration and development programs (e.g., through seismic imaging and well logging). Therefore, the development of fast and inexpensive ‘screening’ techniques tha can provide reliable estimation of permeability at high-resolution (cm-scale) using velocity data could be valuable to exploration/development programs in unconventional reservoirs. A new experimental apparatus is described herein for measuring ultrasonic velocities (P- and S-wave) along the length of slabbed cores of low-permeability rocks at high-resolution (cm-scale). A statistical approach that combines along-core (profile) ultrasonic velocity testing and non-destructive experimental techniques (X-ray fluorescence, mechanical hardness, and profile permeability) is employed to develop predictive models for estimating permeability. Two slabbed cores from the Canadian Montney and Bakken formations, covering multiple geological intervals (tight siltstones/sandstones units), were analyzed for validation purposes. Reasonable agreement is found between log- and lab-derived (ultra)sonic velocity data, indicating similar trends with depth. However, the exact log- and lab-derived (ultra)sonic velocity values are different due to the differences in stress conditions between the field and laboratory measurements and the direction of wave travel. A maximum variation of ±20 m/s is observed for both P- and S-wave velocities when measurements were repeated on the same points, providing evidence of experimental repeatability and reproducibility. Relationships exist between laboratory-measured profile ultrasonic velocities (S-wave), profile permeability, mechanical hardness, and clay content (inferred from elemental composition data). The profile S-wave velocities decrease with increasing permeability (R2 = 0.6, n = 230). Advanced statistical methods (e.g., genetic algorithms) are employed to improve the velocity-permeability relationship and develop models for indirect estimation of permeability from S-wave velocities. The performance of these models is dependent upon lithology and rock fabric (e.g., silt vs. sand, degree of cementation), with a better correlation achieved for intervals with lower porosity and permeability (<±15% maximum discrepancy between measured and predicted permeability values; R2 = 0.78, n = 230). This study introduces a new experimental apparatus, and a practical ‘screening’ workflow, that can be used for permeability prediction using S-wave velocities collected on slabbed cores. This predictive model can be used to estimate permeability below the lower limit (0.001 md) of pressure-decay profile permeability measurements. The findings are beneficial to operators developing tight siltstone/sandstone resources by allowing them to characterize permeability in low-permeability (<0.001 md) intervals for applications such as optimizing stimulation design and subsurface fluid injection.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».